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A TV program recommendation system based on big data

机译:基于大数据的电视节目推荐系统

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With the development of science and technology, more people especially young teenagers do not want to pay more attention to traditional TV programs. Nowadays the challenge of traditional TV station is how to attract the audience's attention, so as to improve the audience rating of tradition TV programs. This paper proposes a recommendation system, which can improve audience rating. This system mainly contains three modules. Data gathering module is responsible for collecting audience rating data about TV programs on the Internet. Data mining module is responsible for analyzing the audience ration data, and finding interesting programs that the audiences want to watch. This program recommendation system is designed to improve audience rating, and catch the attention of audiences. The system is based on massive user data, and data mining algorithms to analyze the user's interests. Compared with traditional recommendation system, it is capable for Big Data and easier for TV station to recommend TV programs in which audiences are interested, as a way to adds vitality to the television industry.
机译:随着科学技术的发展,越来越多的人,尤其是青少年,不希望更多地关注传统电视节目。如今,传统电视台所面临的挑战是如何吸引观众的注意力,从而提高传统电视节目的收视率。本文提出了一种可以提高收视率的推荐系统。该系统主要包含三个模块。数据收集模块负责收集有关互联网上电视节目的收视率数据。数据挖掘模块负责分析观众的口粮数据,并找到观众想要观看的有趣节目。该节目推荐系统旨在提高观众收视率,并引起观众的注意。该系统基于海量用户数据和数据挖掘算法来分析用户兴趣。与传统推荐系统相比,它具有大数据的功能,并且电视台可以更轻松地推荐观众感兴趣的电视节目,从而为电视行业增添活力。

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